Improved multi-reference makeup transfer with localized attention mechanism

Pin-Hua Lee; Chih-Hsien Hsia

doi:10.1117/12.2626005

30 April 2022 Improved multi-reference makeup transfer with localized attention mechanism

Pin-Hua Lee, Chih-Hsien Hsia

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121771W (2022) https://doi.org/10.1117/12.2626005
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Makeup transfer refers to the methodology of transferring the makeup style of a reference image to a source image. Previous works have achieved satisfactory results of transferring the entire style, but multi-reference localized makeup transfer is still challenging due to the diversity of makeup styles as well as a large variety of image content. Our method builds upon image segmentation in order to detect the facial silhouette of the portraits. In this study, an end-to-end multireference makeup transfer framework that generates the output image given multiple reference images. The deep learning (DL) network successfully applies the style from the desired regions of the target reference image to the source image without damaging the original facial features. As demonstrated in the experiment results, the makeup transfer utilizing partial style transfer, and achieve state-of-the-art performance on a wide range of makeup styles.

Citation Download Citation

Pin-Hua Lee and Chih-Hsien Hsia "Improved multi-reference makeup transfer with localized attention mechanism", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121771W (30 April 2022); https://doi.org/10.1117/12.2626005

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KEYWORDS

Edge detection

Convolution

Neural networks

Image processing

Information science

Machine vision

Computer vision technology

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